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“The ECCO Near Real-Time Ocean Data Assimilation System”

“The ECCO Near Real-Time Ocean Data Assimilation System”. I.Fukumori, B.Tang, Z.Xing, D.Menemenlis, O.Wang, S.Ricci, T.Lee, and S.Kim (all at JPL). Venice Symposium, 15 March 2006. Ocean State Estimate for January 30, 2006. ECCO Near Real-Time Analysis.

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“The ECCO Near Real-Time Ocean Data Assimilation System”

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  1. “The ECCO Near Real-Time Ocean Data Assimilation System” I.Fukumori, B.Tang, Z.Xing, D.Menemenlis, O.Wang, S.Ricci, T.Lee, and S.Kim (all at JPL) Venice Symposium, 15 March 2006

  2. Ocean State Estimate for January 30, 2006 ECCO Near Real-Time Analysis • Near real-time global nowcastsevery 10-days updated monthly. • Plots @ http://ecco.jpl.nasa.gov/external • SSH (T/P, Jason-1), Temperatureprofiles (XBT, TAO, ARGO, etc), & time-mean sea level (drifters, GRACE) are assimilated. • Estimated controls: external time-variable & time-mean forcing (winds, heat & fresh water fluxes) and mixing parameters. • A hierarchical assimilation system is used (Green’s function, Kalman filter &Smoother, Adjoint method) • Results are availablevia ECCO LAS server from 1993 to present: http://www.ecco-group.org/las I.Fukumori/JPL

  3. ECCO Near Real-Time Assimilation MIT General Circulation Model (MITgcm) (Marshall et al., 1997) • Nonlinear primitive equation model • Adaptable to massively parallel supercomputers • Advanced physics (e.g., KPP & GM mixing) • Global, high resolution (360  224  46) • 73°S~ 73°N • 0.3°10m in tropics warm cold I.Fukumori/JPL

  4. error space ECCO Approximate Kalman filter & smoother Computational simplification evaluating error covariance 1) Time-asymptotic approximation; (JPO, 1993, 1831-1855); error time 2) State reduction (JGR, 1995, 6777-6793); 3) Partitioning (MWR, 2002, 1370-1383); error space I.Fukumori/JPL

  5. Partitioned Kalman Filter & Smoother Partition the assimilation problem into smaller independent elements, allowing estimation of large degrees of freedom without incurring excessive computational requirements. (Fukumori, MWR, 2002) Separate filters for independent regional or physical elements of the model. I.Fukumori/JPL

  6. Application: Temperature Budget Smoothed estimate allows closure of budgets that permits quantitative analysis of ocean’s variability. Mixed-layer temperature budget (Nino3) Temperature (ºC) Year (Kim et al., 2006, submitted)

  7. Application: Temperature Budget Local redistribution can mask net large-scale effects Sum of boundary flux Volume-mean T meridional zonal Sum of local advection (Kim et al., 2006, submitted)

  8. Application: Temperature Budget Vertical processes & effects of high-frequency variability monthly averages (Kim et al., 2006, submitted)

  9. Cube Sphere Global Coarse Kalman Filter Direct application of telescopic coarse Kalman filter (~5º) to a global ~1/6º cubed sphere model;

  10. Cube Sphere Global Coarse Kalman Filter KF sensibly reduces model-data difference; Reduction of Model-Data residual variance vs time & space data data simulation assimilation

  11. Cube Sphere Local Fine Kalman Filter A high-resolution local filter (0.55°x 0.45°) is implemented to constrain eddy-variability in the Agulhas retroflection region. 356 filter grid points within a 13°x 6° region

  12. Cube Sphere Local Fine Kalman Filter Sea level animation; simulation (top), assimilation (bottom).

  13. Cube Sphere Local Fine Kalman Filter The KF on average improves the model simulation; (Reduction in model-data residual variance.) Skill vs Time Skill vs Space

  14. Cube Sphere Local Fine Kalman Filter Some of the apparent degradation is due to a particular coincident eddy in the simulation. simulation Sea level (m) assimilation T/P 1992 year day

  15. Diabatic Kalman Filter (Estimating heat flux & mixing) Vertical 1-dimensional mixing model February Temperature Error Covariance Matrix September

  16. Diabatic Kalman Filter (Estimating heat flux & mixing) Reduction of model-truth difference (twin experiment); “truth” “truth” assimilation “simulation”

  17. Bias Correction (e.g., Time-Mean Wind Error) Time-mean error estimates can be computed using same system matrices that are used in deriving the Kalman filter. The unknown X can be eliminated from the problem by noting Then, and, where

  18. Bias Correction (e.g., Time-Mean Wind Error) Time-Mean Sea Level Niiler and Maximenko Model forced with COADS +150 cm Difference 0 cm -150 cm -10~-20 cm +10~20 cm

  19. Bias Correction (e.g., Time-Mean Wind Error) +0.05 0 -0.05 Estimated Time-Mean Wind Correction (zonal) ERS-1 minus COADS (zonal) 0 0.02 -0.02 N/m^2

  20. Summary & Conclusion • Data assimilated estimates of ocean circulation are being produced continuously. • Near real-time, near-global assimilation • Products available from 1993 to present • Available at http://ecco.jpl.nasa.gov/external/ • Model and assimilation products are conducive to various applications. • Budget analyses (e.g., ENSO) • Mechanisms of variability (e.g., Med Sea oscillation) • Geodetic studies (e.g., polar motion, J2, GRACE) • Assimilation system is being further improved. • Eddy-permitting assimilation • Diabatic filter • Model bias estimation

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